#ggsave("3prime_Val_t6A_plot.pdf", plot_threePrime, width = 10, height = 8, device = cairo_pdf)
set.seed(123) # Set a seed for reproducibility
set_up("RNA_A", "AAACCGUUACCAUUACUGAG", "Data/sgRNA/20nt_data/20ntRNA_134_931_hydrolysis_5uL_5 min_injection.xlsx", "synthetic_RNA", 6000, 0)
## [1] "Starting the analysis on RNA_A with file location in Data/sgRNA/20nt_data/20ntRNA_134_931_hydrolysis_5uL_5 min_injection.xlsx, the sample is classified as synthetic_RNA with a intact mass higher than 6000"
prophet_A5 = prophet(df, theo_5)
prophet_A3 = prophet(df, theo_3)
set_up("RNA_B", "UAUUCAAGUUACACUCAAGA", "Data/sgRNA/20nt_data/20ntRNA_134_931_hydrolysis_5uL_5 min_injection.xlsx", "synthetic_RNA", 6000, 0)
## [1] "Starting the analysis on RNA_B with file location in Data/sgRNA/20nt_data/20ntRNA_134_931_hydrolysis_5uL_5 min_injection.xlsx, the sample is classified as synthetic_RNA with a intact mass higher than 6000"
prophet_B5 = prophet(df, theo_5)
prophet_B3 = prophet(df, theo_3)
set_up("RNA_C", "GCGUACAUCUUCCCCUUUAU", "Data/sgRNA/20nt_data/20ntRNA_134_931_hydrolysis_5uL_5 min_injection.xlsx", "synthetic_RNA", 6000, 0)
## [1] "Starting the analysis on RNA_C with file location in Data/sgRNA/20nt_data/20ntRNA_134_931_hydrolysis_5uL_5 min_injection.xlsx, the sample is classified as synthetic_RNA with a intact mass higher than 6000"
prophet_C5 = prophet(df, theo_5)
prophet_C3 = prophet(df, theo_3)
prophet_A5 = prophet_A5 %>%
mutate(ladder_type = "A5")
prophet_A3 = prophet_A3 %>%
mutate(ladder_type = "A3")
prophet_B5 = prophet_B5 %>%
mutate(ladder_type = "B5")
prophet_B3 = prophet_B3 %>%
mutate(ladder_type = "B3")
prophet_C5 = prophet_C5 %>%
mutate(ladder_type = "C5")
prophet_C3 = prophet_C3 %>%
mutate(ladder_type = "C3")
prophet_all = rbind(prophet_A3, prophet_A5, prophet_B3, prophet_B5, prophet_C3, prophet_C5) %>%
select(monoisotopic_mass, apex_rt, sum_intensity, relative_abundance, ladder_type, base_name, n_position)
# Filter df1
filtered_df <- df%>%
filter(!monoisotopic_mass %in% prophet_all$monoisotopic_mass) %>%
filter(monoisotopic_mass < 6400) %>%
filter(sum_intensity < 100000) #intensity less than 100000, excluding ladder points
sampled_df <- filtered_df %>% sample_n(100) %>%
mutate(ladder_type = "noise", base_name = "", n_position = 1)
figure1_df = rbind(prophet_all, sampled_df)
data <- prophet_all %>% slice(-c(19, 37, 56, 74))
# 绘制 3D 散点图
plot_ly(data, x = ~monoisotopic_mass, y = ~apex_rt, z = ~sum_intensity,
type = "scatter3d", mode = "markers",
marker = list(size = 4, color = "#20B2AA"))
# 保存为 PDF
pdf("3d_plot_ggplot_style.pdf", width = 8, height = 6)
plot_ly(
data,
x = ~monoisotopic_mass,
y = ~apex_rt,
z = ~sum_intensity,
type = "scatter3d",
mode = "markers",
marker = list(
size = 4,
color = "#20B2AA",
line = list(width = 0) # 移除标记边框
)
) %>%
layout(
scene = list(
# 完全隐藏所有坐标轴
xaxis = list(visible = FALSE),
yaxis = list(visible = FALSE),
zaxis = list(visible = FALSE),
# 透明背景设置
bgcolor = 'rgba(0,0,0,0)',
# 移除3D场景的所有装饰
camera = list(
up = list(x = 0, y = 0, z = 1),
center = list(x = 0, y = 0, z = 0),
eye = list(x = 1.25, y = 1.25, z = 1.25)
)
),
# 全局透明设置
paper_bgcolor = 'rgba(0,0,0,0)',
plot_bgcolor = 'rgba(0,0,0,0)',
# 移除所有边距
margin = list(l = 0, r = 0, b = 0, t = 0)
)